{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a9a8431-35e9-4f0b-9a67-48ec37359034",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf1b409b-7e2d-4919-9615-67aff3769328",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('农机作业轨迹测算挑战赛公开数据/trainData.csv')\n",
    "train_label = pd.read_csv('农机作业轨迹测算挑战赛公开数据/trainLabel.csv')\n",
    "\n",
    "test_data = pd.read_csv('农机作业轨迹测算挑战赛公开数据/testData.csv')\n",
    "test_submit = pd.read_csv('提交示例.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e075068-324f-495e-8162-960d84a72a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num          601.500000\n",
       "latitude      29.419376\n",
       "longitude    106.728831\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f15d943-8f00-465d-bf23-c0aa282293b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>29.419273</td>\n",
       "      <td>106.728780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>29.419276</td>\n",
       "      <td>106.728775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>29.419279</td>\n",
       "      <td>106.728770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>29.419282</td>\n",
       "      <td>106.728763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>29.419285</td>\n",
       "      <td>106.728756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1188</td>\n",
       "      <td>29.419284</td>\n",
       "      <td>106.728713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1191</td>\n",
       "      <td>29.419289</td>\n",
       "      <td>106.728698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1194</td>\n",
       "      <td>29.419293</td>\n",
       "      <td>106.728691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1197</td>\n",
       "      <td>29.419299</td>\n",
       "      <td>106.728683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1200</td>\n",
       "      <td>29.419304</td>\n",
       "      <td>106.728674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      num   latitude   longitude\n",
       "0       3  29.419273  106.728780\n",
       "1       6  29.419276  106.728775\n",
       "2       9  29.419279  106.728770\n",
       "3      12  29.419282  106.728763\n",
       "4      15  29.419285  106.728756\n",
       "..    ...        ...         ...\n",
       "395  1188  29.419284  106.728713\n",
       "396  1191  29.419289  106.728698\n",
       "397  1194  29.419293  106.728691\n",
       "398  1197  29.419299  106.728683\n",
       "399  1200  29.419304  106.728674\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "967ba20a-b388-49a1-8958-3110d60b303e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.419271</td>\n",
       "      <td>106.728785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>29.419272</td>\n",
       "      <td>106.728783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>29.419274</td>\n",
       "      <td>106.728779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>29.419275</td>\n",
       "      <td>106.728777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>29.419277</td>\n",
       "      <td>106.728773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>1193</td>\n",
       "      <td>29.419292</td>\n",
       "      <td>106.728693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>1195</td>\n",
       "      <td>29.419295</td>\n",
       "      <td>106.728688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>1196</td>\n",
       "      <td>29.419297</td>\n",
       "      <td>106.728685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>1198</td>\n",
       "      <td>29.419301</td>\n",
       "      <td>106.728680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>1199</td>\n",
       "      <td>29.419302</td>\n",
       "      <td>106.728678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      num   latitude   longitude\n",
       "0       1  29.419271  106.728785\n",
       "1       2  29.419272  106.728783\n",
       "2       4  29.419274  106.728779\n",
       "3       5  29.419275  106.728777\n",
       "4       7  29.419277  106.728773\n",
       "..    ...        ...         ...\n",
       "795  1193  29.419292  106.728693\n",
       "796  1195  29.419295  106.728688\n",
       "797  1196  29.419297  106.728685\n",
       "798  1198  29.419301  106.728680\n",
       "799  1199  29.419302  106.728678\n",
       "\n",
       "[800 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49c88c19-1bd5-4efa-826d-2d6dc768453e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb033c86970>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train_data['latitude'],train_data['longitude'])\n",
    "plt.scatter(train_label['latitude'],train_label['longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f846bac8-157e-45b9-8298-bea37fbde68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>29.419354</td>\n",
       "      <td>106.728641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>29.419360</td>\n",
       "      <td>106.728633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>29.419368</td>\n",
       "      <td>106.728628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>29.419376</td>\n",
       "      <td>106.728622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>29.419387</td>\n",
       "      <td>106.728614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>771</td>\n",
       "      <td>29.419577</td>\n",
       "      <td>106.728675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>774</td>\n",
       "      <td>29.419565</td>\n",
       "      <td>106.728669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>777</td>\n",
       "      <td>29.419554</td>\n",
       "      <td>106.728662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>780</td>\n",
       "      <td>29.419551</td>\n",
       "      <td>106.728654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>783</td>\n",
       "      <td>29.419534</td>\n",
       "      <td>106.728644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num   latitude   longitude\n",
       "0      3  29.419354  106.728641\n",
       "1      6  29.419360  106.728633\n",
       "2      9  29.419368  106.728628\n",
       "3     12  29.419376  106.728622\n",
       "4     15  29.419387  106.728614\n",
       "..   ...        ...         ...\n",
       "256  771  29.419577  106.728675\n",
       "257  774  29.419565  106.728669\n",
       "258  777  29.419554  106.728662\n",
       "259  780  29.419551  106.728654\n",
       "260  783  29.419534  106.728644\n",
       "\n",
       "[261 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "881460c4-a0a6-4d10-a43c-287064c96bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb031b8d520>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(test_data['latitude'],test_data['longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7be0a4aa-d562-4622-bd92-0206339d7930",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submit['latitude'] = np.nan\n",
    "test_submit['longitude'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9e11559-617c-45d7-862c-83595aa8b84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_24192/1090000891.py:1: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  test_submit = pd.concat([test_submit, test_data], 0).sort_values(by='num')\n"
     ]
    }
   ],
   "source": [
    "test_submit = pd.concat([test_submit, test_data], 0).sort_values(by='num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2faf5f79-b37c-48f7-bc4e-537fc85b9b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submit['submit'] = test_submit['latitude'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "132a1f46-61f9-442e-a0fd-4de6961fd3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submit['latitude'] = test_submit['latitude'].fillna(method='ffill').fillna(test_submit['latitude'].dropna().iloc[0])\n",
    "test_submit['longitude'] = test_submit['longitude'].fillna(method='ffill').fillna(test_submit['longitude'].dropna().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36cd969e-58eb-4ef2-a5f8-cee123acd8a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submit[test_submit['submit']][['num', 'latitude', 'longitude']].to_csv('submit.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43a74511-4092-4012-96ac-6bf35a949b1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3.10"
  },
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